Data augmentation is a good solution for this problem as it will minimize the gap between validation and training portions. Augmentation functions such as random rotations or random nonlinear deformations are easy to apply and are fruitful at improving segmentation accuracy in some settings [1]}, [2]}, [3]}. However, these functions have a limited capability to imitate real variations and can be highly subtle to the choice of parameters [4]}. This phenomena has been reported in the paper of skin lesion classification by [5]} and so for liver lesion classification [6]}.
